This paper presents the Expanded Cholesky Method (ECM) for multifrontal (MF) algorithm to solute sparse symmetric non-positive-definite finite element equations. Optimization methods for reducing the extra demand stor...
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ISBN:
(纸本)9781424443437
This paper presents the Expanded Cholesky Method (ECM) for multifrontal (MF) algorithm to solute sparse symmetric non-positive-definite finite element equations. Optimization methods for reducing the extra demand storage space of fill-ins of MF algorithm have been carefully researched. Results show that the non-positive-definite pattern MF algorithm can be used to calculate a class of non-positive-definite electromagnetic problems.
In this paper, an absorbing Fictitious Boundary Condition (FBC) is presented to generate an iterative Domain Decomposition Method (DDM) for analyzing waveguide *** relaxed algorithm is introduced to improve the iterat...
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In this paper, an absorbing Fictitious Boundary Condition (FBC) is presented to generate an iterative Domain Decomposition Method (DDM) for analyzing waveguide *** relaxed algorithm is introduced to improve the iterative convergence. And the matrix equations are solved using the multifrontal algorithm. The resulting CPU time is greatly ***, a number of numerical examples are given to illustrate its accuracy and efficiency.
This paper presents the Expanded Cholesky Method(ECM) for multifrontal (MF) algorithm to solute sparse symmetric non-positive-definite finite element *** methods for reducing the extra demand storage space of fill-ins...
详细信息
This paper presents the Expanded Cholesky Method(ECM) for multifrontal (MF) algorithm to solute sparse symmetric non-positive-definite finite element *** methods for reducing the extra demand storage space of fill-ins of MF algorithm have been carefully *** show that the non-positive-definite pattern MF algorithm can be used to calculate a class of non-positive-definite electromagnetic problems.
This paper presents the Expanded Cholesky Method (ECM) for multifrontal (MF) algorithm to solute sparse symmetric non-positive-definite finite element *** methods for reducing the extra demand storage space of fill-in...
详细信息
This paper presents the Expanded Cholesky Method (ECM) for multifrontal (MF) algorithm to solute sparse symmetric non-positive-definite finite element *** methods for reducing the extra demand storage space of fill-ins of MF algorithm have been carefully *** show that the non-positive-definite pattern MF algorithm can be used to calculate a class of non-positive-definite electromagnetic problems.
A few parallel algorithms for solving triangular systems resulting from parallel factorization of sparse linear systems have been proposed and implemented recently. We present a detailed analysis of parallel complexit...
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ISBN:
(纸本)0897918622
A few parallel algorithms for solving triangular systems resulting from parallel factorization of sparse linear systems have been proposed and implemented recently. We present a detailed analysis of parallel complexity and scalability of the best of these algorithms and the results of its implementation on up to 256 processors of the Cray T3D parallel computer. It has been a common belief that parallel sparse triangular solvers are quite unscalable due to a high communication to computation ratio. Our analysis and experiments show that, although not as scalable as the best parallel sparse Cholesky factorization algorithms, parallel sparse triangular solvers can yield reasonable speedups in runtime on hundreds of processors. We also show that for a wide class of problems, the sparse triangular solvers described in this paper are optimal and are asymptotically as scalable as a dense triangular solver.
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